A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems

Elhenawy, Mohammed; Rakha, Hesham A.; Bichiou, Youssef; Masoud, Mahmoud; Glaser, Sébastien; Pinnow, Jack; Stohy, Ahmed

doi:10.3390/su132313433

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…A higher connectivity count, denoted by c ij (t), indicates a stronger borrowing and returning relationship between the stations. The calculation of the interstation connectivity count (c ij (t)) between stations i and j within unit time t is shown in Equation (5).…”

Section: Definition Of Parametersmentioning

confidence: 99%

“…High-capacity and rapid public transportation modes such as metro and buses address the long-distance travel needs of urban residents [4]. However, the site selection results of existing public transportation hubs evidently fail to meet residents' 'last-mile' travel demands [5]. Hence, public bicycles emerge as the optimal solution to address the 'lastmile' travel issue [6].…”

Section: Introductionmentioning

confidence: 99%

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Public Bicycle Dispatch Method Based on Spatiotemporal Characteristics of Borrowing and Returning Demands

Liu,

Wu,

Tang

et al. 2024

Sustainability

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Public bicycle systems (PBSs) serve as the ‘last mile’ of public transportation for urban residents, yet the problem of the difficulty in borrowing and returning bicycles during peak hours remains a major bottleneck restricting the intelligent and efficient operation of public bicycles. Previous studies have proposed reasonable models and efficient algorithms for optimizing public bicycle scheduling, but there is still a lack of consideration for actual road network distances between stations and the temporal characteristics of demand at rental points in the model construction process. Therefore, this paper aims to construct a public bicycle dispatch framework based on the spatiotemporal characteristics of borrowing and returning demands. Firstly, the spatiotemporal distribution characteristics of borrowing and returning demands for public bicycles are explored, the origin–destination (OD) correlation coefficients are defined, and the intensity of connections between rental point areas is analyzed. Secondly, based on the temporal characteristics of rental point demands, a random forest prediction model is constructed with weather factors, time characteristics, and rental point locations as feature variables, and station bicycle-borrowing and -returning demands as the target variable. Finally, bicycle dispatch regions are delineated based on actual path distances between stations and OD correlation coefficients, and a public bicycle regional dispatch optimization method is established. Taking the PBS in Ningbo City as an example, the balancing optimization framework proposed in this paper is validated. The results show that the regional dispatch optimization method proposed in this paper can achieve optimized dispatch of public bicycles during peak hours. Additionally, compared with the Taboo search algorithm (TSA), the genetic algorithm (GA) exhibits a 11.1% reduction in rebalancing time and a 40.4% reduction in trip cost.

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Section: Definition Of Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Public Bicycle Dispatch Method Based on Spatiotemporal Characteristics of Borrowing and Returning Demands

Liu,

Wu,

Tang

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Despite delivering optimum answers, due to exponential complexity, appropriate procedures require a disproportionately long computation for large-scale problems. On the other hand, heuristic techniques for PDP and VRP can handle large-scale cases with a relatively good processing efficiency [ 24 – 28 ]. To cope with large-scale PDP, Li and Lim [ 29 ] proposed a tabu-embedded simulated annealing technique based on the shift, exchange and devised rearrange operators.…”

Section: Related Workmentioning

confidence: 99%

Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning

et al. 2022

Self Cite

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In this study, we propose a general method for tackling the Pickup and Drop-off Problem (PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our aim is to reduce the overall tour length traveled by an agent while remaining within the truck’s capacity restrictions and adhering to the node-to-node relationship. In such instances, the agent does not allow any drop-off points to be serviced if the truck is empty; conversely, if the vehicle is full, the agent does not allow any products to be picked up from pickup points. In our approach, this challenge is solved using machine learning-based models. Using HPNs as our primary model allows us to gain insight and tackle more complicated node interactions, which simplified our objective to obtaining state-of-art outcomes. Our experimental results demonstrate the effectiveness of the proposed neural network, as we achieve the state-of-art results for this problem as compared with the existing models. We deal with two types of demand patterns in a single type commodity problem. In the first pattern, all demands are assumed to sum up to zero (i.e., we have an equal number of backup and drop-off items). In the second pattern, we have an unequal number of backup and drop-off items, which is close to practical application, such as bike sharing system rebalancing. Our data, models, and code are publicly available at Solving Pickup and Dropoff Problem Using Hybrid Pointer Networks with Deep Reinforcement Learning.

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“…Estos vehículos incluyen bicicletas eléctricas (e-bikes), monopatines eléctricos (e-scooters) y motocicletas eléctricas. La manera como viajan las personas en áreas urbanas está cambiando rápidamente, a medida que este concepto es adoptado y promovido para obtener un sistema de transporte más sostenible [4].…”

Section: Introductionunclassified

Study of the Energy Efficiency of an Urban E-Bike Charged with a Standalone Photovoltaic Solar Charging Station and its Compliance with the Ecuadorian Grid Code No. ARCERNNR – 002/20

Iñiguez-Morán

Villa-Ávila

Ochoa

et al. 2023

ings

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E-bikes are an emerging sustainable means of transportation, if adopted massively, they can help face the challenges of human mobility in urban centers worldwide. In Cuenca, Ecuador, the local government built cycle routes (13.47 km) connecting strategic points to facilitate and encourage sustainable mobility. However, the effective implementation of the electromobility strategies at a large scale entails impacts on the power grid, like the increase in the energy demand and the possible decrease of the energy quality due to the harmonic distortion that characterizes the battery's charging current. This research aims to obtain a primary input to evaluate such impacts through an energy efficiency study of an urban e-bike charged by a standalone solar photovoltaic charging station implemented in the Microgrid Laboratory of Universidad de Cuenca. The methodology includes the experimental characterization of the battery's charging regime, the vehicle's energy efficiency calculation, and the evaluation of its compliance with Ecuadorian grid code No. ARCERNNR – 002/20. Results show that the battery's charger performs a charging regime standardized by German regulations, delivering 92% of charge in 4.82 hours. The e-bike's calculated average energy efficiency is 2.18 kWh/100 miles or 73.77 m/Wh, and a fuel economy of 1545.1 MPGe. Finally, the magnitude of the first four odd harmonic components and the total harmonic distortion of the charging current exceeds the limits established by the grid code in force.

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A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems

Cited by 3 publications

References 34 publications

Public Bicycle Dispatch Method Based on Spatiotemporal Characteristics of Borrowing and Returning Demands

Public Bicycle Dispatch Method Based on Spatiotemporal Characteristics of Borrowing and Returning Demands

Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning

Study of the Energy Efficiency of an Urban E-Bike Charged with a Standalone Photovoltaic Solar Charging Station and its Compliance with the Ecuadorian Grid Code No. ARCERNNR – 002/20

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